Understand the science of reading and how to implement evidence-based instruction to increase the reading and writing achievement of pre-K-6 students, including those at risk for reading difficulties. Fully revised and updated, this core text covers the research base for structured literacy instruction and practical guidance on the essential…

Researchers in career and technical education often ignore more effective ways of reporting and treating missing data and instead implement traditional, but ineffective, missing data methods (Gemici, Rojewski, & Lee, 2012). The recent methodological, and even the non-methodological, literature has increasingly emphasized the importance of…

This paper presents an argument against the wider adoption of complex forms of data analysis, using multi-level modeling (MLM) as an extended case study. MLM was devised to overcome some deficiencies in existing datasets, such as the bias caused by clustering. The paper suggests that MLM has an unclear theoretical and empirical basis, has not led…

Over the past five years, the No Child Left Behind Act, with its mandates for collecting and documenting student achievement statistics, has fueled a surge in the volume of data collected by schools, districts, and state education departments. This information explosion has gone hand in hand with technology's growing affordability and…

Data-driven decision-making (D3M) appears to be the new buzz phrase for this century, the information age. On the education front, teachers and administrators are engaging in data-centered dialog in grade-level meetings, lounges, hallways, and classrooms as they brainstorm toward closing the gap in student achievement. Clearly, such discussion…

A follow-up study by officials of the Council for Aid to Education uncovered a major error in the Internal Revenue Service's 1987 corporate-contributions data. The new figures, representing contributions from 1986 to 1989, reflect a continued but less dramatic growth in corporate philanthropy. (MLW)

Maintains that there is an inconsistency in the regression analysis of the data presented in Maureen J. Lage and Michael Treglia's 1996 article, "The Impact of Integrating Scholarship on Women into Introductory Economics." The authors admit the error, correct it, and report no difference in their original conclusions. (MJP)